Story
Databricks
SNCFSNCF Standardizes Feature Pipelines on Databricks
SNCF, an industrials organization in France, uses Unity Catalog from Databricks to support applied machine learning for data scientists and ML engineers.
Teams at SNCF moved feature pipelines onto Databricks after local tools started to collide.
“Data scientists and ML engineers asked for a shared way to run applied machine learning without freezing local judgment.”
“Databricks provides a lakehouse platform for data engineering, analytics, and AI, with Unity Catalog and Mosaic AI for governed models.”
Independent write-up. Figures come from public sources or are illustrative.
Value results
| Category | Value result |
|---|---|
| Productivity | Fewer stalled items because feature pipelines has a clear owner |
| Risk and compliance | Unity Catalog is the governed place data scientists and ML engineers use for applied machine learning |
| Capability | New joiners can see how applied machine learning actually runs |
Story
Industrials work at SNCF spans more than one site, even when headquarters sits in France. Feature pipelines was splitting across regional habits. Data scientists and ML engineers asked for a shared way to run applied machine learning without freezing local judgment.
Databricks (Unity Catalog) is what they standardized on. Databricks provides a lakehouse platform for data engineering, analytics, and AI, with Unity Catalog and Mosaic AI for governed models. SNCF uses it as the system of record for feature pipelines, with data scientists and ML engineers as the primary operators and other groups coming in through the same queue.
Leaders get a picture they can actually walk. Teams get fewer mystery statuses. The story is about operating change, not an unpublished percentage.
